The use of Monte Carlo simulation has been retarded by the haze of confusion stemming from unfamiliarity with the tool and the way it is often presented. This article presents an overview of how Monte Carlo simulation can be used in a variety of management situations, and outlines characteristics of and requirements for its successful application. Applications of Monte Carlo procedures in production economics and in production planning and inventory control are illustrated, with a view to improving understanding of Monte Carlo methods, as a toot of significant value in research and practical business management.LES MODELES DE SIMULATION DE LA TECHNIQUE MONTE CARLO ‐ L'usage de la technique Monte Carlo a ralenti quelque peu, à cause de l'incertitude et la façon dont le sujet a déjà été présenté. Cet etude présents un aperçu de cette technique et son usage dans diverses situations administrates. Elle souligne aussi quelques caractéristiques pour une meillewe application. Les méthodes à employer dans le dirigisme économique sont aussi illustréesSUMMARYMonte Carlo simulation is not a substitute for proper theoretical model construction, nor is it a substitute for proper experimental design and statistical analysis. Rather Monte Carlo simulation is a method of performing experiments on functionally expressed models. As such, careful attention should be given to primary model construction, experimental design and statistical analysis. Although simulation procedures in general allow testing of various policies, decisions and rules relating to an operating system, Monte Carlo simulation facilitates a more vaned type of experimentation. This results from the random generation of finite observations for otherwise undefined functional relations. Monte Carlo is also well adapted to situations requiring an approximation of the stochastic influences often found in real world operating and decision systems. This is especially relevant when theoretical analysis is unable to relate certain phenomena. Monte Carlo techniques Frovide a means of experimentation from which statements of statistical inference may be made. Such inferences will facilitate logical consistency of analytical models. Monte Carlo techniques provide a means of performing experiments from whch statements of statistical infer